Yunhyong Kim

We use classifiers independently modeled on three groups of features to examine six genre classes to show that the strongest features for making one classification is not necessarily the best features for carrying out another classification. Expand

This paper presents an overview of automatic methods for building domain knowledge structures (domain models) from text collections. Expand

This paper describes the KRYS I corpus, consisting of documents classified into 70 genre classes. Expand

We introduce the Credible Live Evaluation of Archive Readiness (CLEAR) method, a set of metrics to quantify the level of archivability of any website. Expand

AutoEval is an evaluation methodology that assesses the quality of query modifications generated by a model using the query logs of past user interactions with a search engine. Expand

This paper presents results in automated genre classification of digital documents in PDF format. Expand

Metadata creation is a crucial aspect of the ingest of digital materials into digital libraries. Expand

Document classification is one of the most fundamental steps in enabling the search, selection, and ranking of digital material according to its relevance in answering a predefined search. Expand

This paper builds on the work presented at the ECDL 2006 in automated genre classification as a step toward automating metadata extraction from digital documents for ingest into digital repositories such as those run by archives. Expand

In this paper we discuss archival storage formats from the point of view of digital curation and preservation. Expand